Institute for Chemical and Bioengineering, ETH Zurich, 8093, Zurich, Switzerland.
Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.
BMC Bioinformatics. 2021 Mar 4;22(1):108. doi: 10.1186/s12859-021-04046-2.
Knowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes.
In this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ uses a gene regulatory network model to identify direct perturbations to the transcription of genes using gene expression data. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course expression profiles, as well as leverage information on the gene network structure. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains.
DeltaNeTS+ is a powerful network analysis tool for inferring gene targets from gene expression profiles. As demonstrated in the case studies, by incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~ 10 s). Integrating static and dynamic expression data with transcriptional network structure extracted from genomic information, as enabled by DeltaNeTS+, is crucial toward personalized medicine, where treatments can be tailored to individual patients. DeltaNeTS+ can be freely downloaded from http://www.github.com/cabsel/deltanetsplus .
了解疾病和药物的分子靶点对于阐明疾病发病机制和药物作用机制，以及推动药物发现和治疗方案制定至关重要。在这方面，高通量基因转录谱分析已成为一项领先技术，可生成疾病或药物化合物引起的基因转录变化的全基因组数据。然而，基于差异基因表达来识别直接的基因靶点，特别是在间接（下游）效应的背景下，由于调控基因转录过程的基因调控网络的复杂性，这是困难的。
在这项工作中，我们开发了一种网络分析方法，称为 DeltaNeTS+，用于从基因转录谱中推断药物和疾病的直接基因靶点。DeltaNeTS+ 使用基因调控网络模型，使用基因表达数据识别对基因转录的直接扰动。重要的是，DeltaNeTS+ 能够结合稳态和时程表达谱，以及利用基因网络结构的信息。我们通过使用复杂生物体（包括秀丽隐杆线虫和人细胞系（T 细胞和 Calu-3））中的基因表达数据证明了 DeltaNeTS+ 在预测基因靶点方面的强大功能。更具体地，在应用于甲型流感 H1N1（猪流感）和 H5N1（禽流感）感染的时程基因表达谱时，DeltaNeTS+ 揭示了两种流感病毒株引起的动态细胞扰动的关键差异。
DeltaNeTS+ 是一种从基因表达谱中推断基因靶点的强大网络分析工具。如案例研究所示，通过整合基因网络结构的可用信息，DeltaNeTS+ 可从小样本量（~10 个）中准确预测直接基因靶点。通过 DeltaNeTS+ 实现的将静态和动态表达数据与从基因组信息中提取的转录网络结构相结合，对于个性化医疗至关重要，在这种医疗中，可以根据个体患者的情况量身定制治疗方案。DeltaNeTS+ 可从 http://www.github.com/cabsel/deltanetsplus 免费下载。
